82,729 research outputs found
Satellite-based delivery of educational content to geographically isolated communities: A service based approach
Enabling learning for members of geographically
isolated communities presents benefits in terms of
promoting regional development and cost savings for governments and companies. However, notwithstanding recent advances in e-Learning, from both technological and pedagogical perspectives, there are very few, if any,
recognised methodologies for user-led design of satellite-based e-learning infrastructures. In this paper, we present a methodology for designing a satellite and wireless based network infrastructure and learning services to support distance learning for such isolated communities. This methodology entails (a) the involvement of community members in the development of targeted learning services from an early stage, and (b) a service-oriented approach to learning solution deployment. Results show, that, while the technological premises of distance learning can be
accommodated by hybrid satellite/wireless infrastructures,this has to be complemented with (a) high-quality audio–visual educational material, and (b) the opportunity for community members to interact with other community
members either as groups (common-room oriented scenarios) or individuals (home-based scenarios), thus providing an impetus for learner engagement in both formal and informal activities
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
While modern parallel computing systems provide high performance resources,
utilizing them to the highest extent requires advanced programming expertise.
Programming for parallel computing systems is much more difficult than
programming for sequential systems. OpenMP is an extension of C++ programming
language that enables to express parallelism using compiler directives. While
OpenMP alleviates parallel programming by reducing the lines of code that the
programmer needs to write, deciding how and when to use these compiler
directives is up to the programmer. Novice programmers may make mistakes that
may lead to performance degradation or unexpected program behavior. Cognitive
computing has shown impressive results in various domains, such as health or
marketing. In this paper, we describe the use of IBM Watson cognitive system
for education of novice parallel programmers. Using the dialogue service of the
IBM Watson we have developed a solution that assists the programmer in avoiding
common OpenMP mistakes. To evaluate our approach we have conducted a survey
with a number of novice parallel programmers at the Linnaeus University, and
obtained encouraging results with respect to usefulness of our approach
Managing evolution and change in web-based teaching and learning environments
The state of the art in information technology and educational technologies is evolving constantly.
Courses taught are subject to constant change from organisational and subject-specific reasons. Evolution
and change affect educators and developers of computer-based teaching and learning environments alike –
both often being unprepared to respond effectively. A large number of educational systems are designed
and developed without change and evolution in mind. We will present our approach to the design and
maintenance of these systems in rapidly evolving environments and illustrate the consequences of evolution
and change for these systems and for the educators and developers responsible for their implementation and
deployment. We discuss various factors of change, illustrated by a Web-based virtual course, with the
objective of raising an awareness of this issue of evolution and change in computer-supported teaching and
learning environments. This discussion leads towards the establishment of a development and management
framework for teaching and learning systems
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Design of generic modular reconfigurable platforms (GMRPS) for a product-oriented micro manufacturing system
With the proposition of the concept of product-service systems, many manufacturers are focusing on selling services or functionality rather than products. Industrial production is shifting production models from mass production to mass customization and highly personalized needs. As a result, there is a tendency for manufacturing system suppliers to develop product-oriented systems to responsively cope with the dynamic fast moving competitive market. The key features of such a manufacturing system are the reconfigurability and adaptability, which can enable the system respond to the changeable needs of customers quickly and adaptively. Therefore, one of the challenges for the micro manufacturing system provider has been the design of a reconfigurable machine platform which will provide the functionalities and flexibility required by the product-oriented systems.
In this paper, a new micro manufacturing platform, i.e. a generic modular reconfigurable platform (GMRP) is proposed in order to provide an effective means for fabrication of high quality micro products at low cost in a responsive manner. The GMRP-based system aims to be a product-oriented reconfigurable, highly responsive manufacturing system particularly for high value nano/micro manufacturing purposes. To reuse components and decrease material consumption, GMRP is characterized by hybrid micro manufacturing processes, modularity of key components, and reconfigurability of machine platforms and key components. Furthermore, a practical methodology for the design of reconfigurable machine platforms is discussed against the requirements from product-driven micro manufacturing and its extension for adaptive production
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT
This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p
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Design Space Exploration in Cyber-Physical Systems
Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations
Cost modelling for cloud computing utilisation in long term digital preservation
The rapid increase in volume of digital information can cause concern among organisations regarding manageability, costs and security of their information in the long-term. As cloud computing technology is often used for digital preservation purposes and is still evolving, there is difficulty in determining its long-term costs. This paper presents the development of a generic cost model for public and private clouds utilisation in long term digital preservation (LTDP), considering the impact of uncertainties and obsolescence issues. The cost model consists of rules and assumptions and was built using a combination of activity based and parametric cost estimation techniques. After generation of cost breakdown structures for both clouds, uncertainties and obsolescence were categorised. To quantify impacts of uncertainties on cost, three-point estimate technique was employed and Monte Carlo simulation was applied to generate the probability distribution on each cost driver. A decision support cost estimation tool with dashboard representation of results was developed
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
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